

When interpreting the results of fitting a mixed model, interpreting the P values is the same as twoway ANOVA. So read the general page on interpreting twoway ANOVA results first. Also read the general page on the assumption of sphericity, and assessing violations of that assumption with epsilon.
The mixed effects model treats the different subjects (participants, litters, etc) as a random variable. The residual random variation is also random. The effect of all random variables is quantified with its variation. Prism presents the variation as both a SD and a variance (which is the SD squared). You, or more likely your statistical consultant, may be interested in these values to understand the relative variation among subjects responses (the subject variance) and within the repeated responses from the same subject (the residual variance). compare with other programs.
A repeatedmeasures experimental design can be very powerful, as it controls for random factors that cause or unmeasured variability between subjects. If the matching is effective, the repeatedmeasures test will yield a smaller P value than an ordinary ANOVA. The repeatedmeasures test is more powerful because it separates betweensubject variability from withinsubject variability. If the pairing is ineffective, however, the repeatedmeasures test can be less powerful because it has fewer degrees of freedom.
Prism tests whether the matching was effective and reports a P value. This P value comes from a chisquare statistic that is computed by comparing the fit of the full mixed effects model to a simpler model without accounting for repeated measures. If this P value is low, you can conclude that the matching was effective. If the P value is high, you can conclude that the matching was not effective and should reconsider your experimental design for your next study.
Prism expresses the goodnessoffit in a few ways. These will only be meaningful to someone who understand mixed effects models deeply. Most scientists will ignore these results or not check the option so they never get reported. But some journals may ask you to report at least one measure of goodness of fit.
If you checked the option to not accept the assumption of sphericity, Prism does two things differently.
•It applies the correction of Geisser and Greenhouse. You'll see smaller degrees of freedom, which usually are not integers. The corresponding P value is higher than it would have been without that correction.
•It reports the value of epsilon, which is a measure of how badly the data violate the assumption of sphericity.
Learn about multiple comparisons tests after repeated measures ANOVA.
Before interpreting the results, review the analysis checklist.